Mahalanobis distance–based kernel supervised machine learning in spectral dimensionality reduction for hyperspectral imaging remote sensing

Jing Liu,Yulong Qiao

doi:10.1177/1550147720968467

Abstract

Spectral dimensionality reduction is a crucial step for hyperspectral image classification in practical applications. Dimensionality reduction has a strong influence on image classification performance with the problems of strong coupling features and high band correlation. To solve these issues, we propose the Mahalanobis distance–based kernel supervised machine learning framework for spectral dimensionality reduction. With Mahalanobis distance matrix–based dimensional reduction, the coupling relationship between features and the elimination of the scale effect are removed in low-dimensional feature space, which benefits the image classification. The experimental results show that compared with other methods, the proposed algorithm demonstrates the best accuracy and efficiency. The Mahalanobis distance–based multiples kernel learning achieves higher classification accuracy than the Euclidean distance kernel function. Accordingly, the proposed Mahalanobis distance–based kernel supervised machine learning method performs well with respect to the spectral dimensionality reduction in hyperspectral imaging remote sensing.

Highlights

Hyperspectral sensing remote systems are widely used in energy exploration, social safety, military monitoring, and other areas
Under the Indian Pine data set, while the classification accuracy increased by 3%, the classifier training time was reduced by 23%, and the test time was reduced by 31%
Under the University of Pavia data set, while the classification accuracy increased by 1.5%, the classifier training time was reduced by 25%, and the test time was reduced by 33%

Summary

Introduction

Hyperspectral sensing remote systems are widely used in energy exploration, social safety, military monitoring, and other areas. Hyperspectral remote sensing provides accurate representations of the different materials with high spectral resolution on airborne and satellite platforms. Machine learning is a promising method for hyperspectral data analysis. Since the relationship between spectral curves is nonlinear and complex, spectral classification is a classic complex and nonlinear problem. Among these machine learning methods, a feasible and effective nonlinear method utilizes a kernel technique. As the spectral resolution increases, the coupling between the spectral bands becomes stronger, and the correlation becomes greater. The different spectral bands have different weights on the particular classification problem. Many classification algorithms do not use College of Information and Communication Engineering, Harbin Engineering University, Harbin, China

Methods

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Discussion

Conclusion